Multiple Imputation Strategies for Multiple Group Structural Equation Models

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Introduction to Latent Class Analysis With Applications (2017, methodology): “Because LC models are multiple group models where group membership must be inferred from the data, Enders and Gottschall (2011) argued that it is not possible to use any multiple imputation method to preserve group differences in the mean and covariance structure across hidden latent classes.”
Attrition in developmental psychology (2017, result): “For example, failure to specifically consider interactive effects in the imputation phase, which may require a particular application of MI, may bias parameter estimates in the subsequent analysis phase (Enders, Baraldi, & Cham, 2014; Enders & Gottschall, 2011).”
Multiple imputation as a flexible tool for missing data handling in clinical research. (2017, methodology): “…this step resembles the problematic impute-then-transform approach, separate-group imputation allows the covariance structure to differ across groups, thereby preserving all possible two-way interactions between the grouping variable and the imputation model variables (Enders & Gottschall, 2011).”